Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997

1997

Earlier work this paper cites.

Y. LeCun, “The mnist database of handwritten digits,” http://yann. lecun. com/exdb/mnist/ , 1998

1998

Earlier work this paper cites.

L. Jacob, J.-p. Vert, and F. R. Bach, “Clustered multi-task learning: A convex formulation,” in Advances in neural information processing systems , 2009, pp. 745–752

2009

Earlier work this paper cites.

A. Kumar and H. Daume III, “Learning task grouping and overlap in multi-task learning,” arXiv preprint arXiv:1206.6417 , 2012

Original

2012

Earlier work this paper cites.

A. Krizhevsky, V. Nair, and G. Hinton, “The cifar-10 dataset,” online: http://www. cs. toronto. edu/kriz/cifar. html , 2014

2014

Earlier work this paper cites.

M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . ACM, 2015, pp. 1322–1333

2015

Earlier work this paper cites.

H. B. McMahan, E. Moore, D. Ramage, S. Hampson et al. , “Communication-efficient learning of deep networks from decentralized data,” arXiv preprint arXiv:1602.05629 , 2016

Original

2016

Earlier work this paper cites.

J. Konečnỳ, H. B. McMahan, D. Ramage, and P. Richtárik, “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527 , 2016

Original

2016

Earlier work this paper cites.

V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, and M. Jaggi, “Cocoa: A general framework for communication-efficient distributed optimization,” arXiv preprint arXiv:1611.02189 , 2016

Original

2016

Earlier work this paper cites.

J. Konečnỳ, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492 , 2016

Original

2016

Earlier work this paper cites.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy preserving machine learning.” IACR Cryptology ePrint Archive , vol. 2017, p. 281, 2017

2017

Earlier work this paper cites.

S. Ruder, “An overview of multi-task learning in deep neural networks,” arXiv preprint arXiv:1706.05098 , 2017

Original

2017

Earlier work this paper cites.

V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” in Advances in Neural Information Processing Systems , 2017, pp. 4424–4434

2017

Earlier work this paper cites.

Y. Lin, S. Han, H. Mao, Y. Wang, and W. J. Dally, “Deep gradient compression: Reducing the communication bandwidth for distributed training,” arXiv preprint arXiv:1712.01887 , 2017

Original

2017

Cited alongside, same era.

B. Hitaj, G. Ateniese, and F. Perez-Cruz, “Deep models under the gan: information leakage from collaborative deep learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 603–618

2017

Cited alongside, same era.

A. K. Sahu, T. Li, M. Sanjabi, M. Zaheer, A. Talwalkar, and V. Smith, “On the convergence of federated optimization in heterogeneous networks,” arXiv preprint arXiv:1812.06127 , 2018

Original

2018

Cited alongside, same era.

S. Caldas, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar, “Leaf: A benchmark for federated settings,” arXiv preprint arXiv:1812.01097 , 2018

Original

2018

Cited alongside, same era.

K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, H. B. McMahan et al. , “Towards federated learning at scale: System design,” arXiv preprint arXiv:1902.01046 , 2019

Original

2019

Closest in time.

T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” arXiv preprint arXiv:1908.07873 , 2019

Original

2019

Closest in time.

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of fedavg on non-iid data,” arXiv preprint arXiv:1907.02189 , 2019

Original

2019

Closest in time.

Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, p. 12, 2019

2019

Closest in time.